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. 2022 Jan 7;12:192. doi: 10.1038/s41598-021-03946-w

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© The Author(s) 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Analysis of weighted gene co-expression networks for the construction of PCa. (A). Dendrogram combining clinical data and transcriptional expression profiles for sample Euclidean distance clustering. (B). Analysis of soft threshold power for WGCNA: (left) Analysis of the scale-free matching index (β) for various soft threshold powers. (Right) Analysis of the average connectivity of various soft threshold powers. (C). Dendrogram of all differentially expressed genes clustered based on the measure of variability (1-TOM). The colors bands show the results obtained from the automated individual cluster analysis. (D). The heat map shows the relationship between MEs and clinical characteristics. Each cell contains the corresponding correlation coefficient and p-value. (E, F). Scatterplot of gene significance versus module affiliation in the three selected key modules blue, green modules.